{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data directly from ESGF nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This notebook demonstrates searching for and loading CMIP simulations from ESGF, using the <a href=\"https://github.com/esgf2-us/intake-esgf\" style=\"color: blue; text-decoration: underline;\">intake_esgf</a> package.\n",
    "* This code is taken from the intake-esgf <a href=\"https://intake-esgf.readthedocs.io/en/latest/beginner.html\" style=\"color: blue; text-decoration: underline;\">tutorial</a>. Please refer there for a fuller explanation of each step. \n",
    "* The data search uses the standard CMIP directory structure, see <a href=\"https://help.ceda.ac.uk/article/4801-cmip6-data#CMIP6%20data%20structure\" style=\"color: blue; text-decoration: underline;\">here</a> for an explanation. We make use of xarray; see <a href=\"https://tutorial.xarray.dev/intro.html\" style=\"color: blue; text-decoration: underline;\">this turorial</a> for an introduction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting intake_esgf\n",
      "  Downloading intake_esgf-2024.12.7-py3-none-any.whl.metadata (4.0 kB)\n",
      "Requirement already satisfied: pandas in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (2.2.3)\n",
      "Requirement already satisfied: dask in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (2025.1.0)\n",
      "Requirement already satisfied: xarray in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (2025.1.1)\n",
      "Requirement already satisfied: netCDF4 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (1.7.2)\n",
      "Collecting globus-sdk (from intake_esgf)\n",
      "  Downloading globus_sdk-3.55.0-py3-none-any.whl.metadata (3.3 kB)\n",
      "Requirement already satisfied: requests in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (2.32.3)\n",
      "Requirement already satisfied: tqdm[notebook] in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (4.67.1)\n",
      "Requirement already satisfied: pyyaml in /srv/conda/envs/notebook/lib/python3.12/site-packages (from intake_esgf) (6.0.2)\n",
      "Requirement already satisfied: click>=8.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (8.1.8)\n",
      "Requirement already satisfied: cloudpickle>=3.0.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (3.1.1)\n",
      "Requirement already satisfied: fsspec>=2021.09.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (2024.12.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (24.2)\n",
      "Requirement already satisfied: partd>=1.4.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (1.4.2)\n",
      "Requirement already satisfied: toolz>=0.10.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from dask->intake_esgf) (1.0.0)\n",
      "Requirement already satisfied: pyjwt<3.0.0,>=2.0.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pyjwt[crypto]<3.0.0,>=2.0.0->globus-sdk->intake_esgf) (2.10.1)\n",
      "Requirement already satisfied: cryptography!=3.4.0,>=3.3.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from globus-sdk->intake_esgf) (43.0.1)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests->intake_esgf) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests->intake_esgf) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests->intake_esgf) (1.26.19)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests->intake_esgf) (2024.12.14)\n",
      "Requirement already satisfied: cftime in /srv/conda/envs/notebook/lib/python3.12/site-packages (from netCDF4->intake_esgf) (1.6.4)\n",
      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.12/site-packages (from netCDF4->intake_esgf) (2.0.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas->intake_esgf) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas->intake_esgf) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas->intake_esgf) (2025.1)\n",
      "Requirement already satisfied: ipywidgets>=6 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from tqdm[notebook]->intake_esgf) (8.1.5)\n",
      "Requirement already satisfied: cffi>=1.12 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from cryptography!=3.4.0,>=3.3.1->globus-sdk->intake_esgf) (1.17.1)\n",
      "Requirement already satisfied: comm>=0.1.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.2.2)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=6->tqdm[notebook]->intake_esgf) (8.17.2)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=6->tqdm[notebook]->intake_esgf) (5.14.3)\n",
      "Requirement already satisfied: widgetsnbextension~=4.0.12 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=6->tqdm[notebook]->intake_esgf) (4.0.13)\n",
      "Requirement already satisfied: jupyterlab_widgets~=3.0.12 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=6->tqdm[notebook]->intake_esgf) (3.0.13)\n",
      "Requirement already satisfied: locket in /srv/conda/envs/notebook/lib/python3.12/site-packages (from partd>=1.4.0->dask->intake_esgf) (1.0.0)\n",
      "Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas->intake_esgf) (1.17.0)\n",
      "Requirement already satisfied: pycparser in /srv/conda/envs/notebook/lib/python3.12/site-packages (from cffi>=1.12->cryptography!=3.4.0,>=3.3.1->globus-sdk->intake_esgf) (2.22)\n",
      "Requirement already satisfied: decorator in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.19.2)\n",
      "Requirement already satisfied: matplotlib-inline in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.1.7)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (3.0.38)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (2.19.1)\n",
      "Requirement already satisfied: stack-data in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.6.3)\n",
      "Requirement already satisfied: pexpect>4.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (4.9.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.8.4)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /srv/conda/envs/notebook/lib/python3.12/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.2.13)\n",
      "Requirement already satisfied: executing>=1.2.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (2.1.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (3.0.0)\n",
      "Requirement already satisfied: pure_eval in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=6->tqdm[notebook]->intake_esgf) (0.2.3)\n",
      "Downloading intake_esgf-2024.12.7-py3-none-any.whl (36 kB)\n",
      "Downloading globus_sdk-3.55.0-py3-none-any.whl (408 kB)\n",
      "Installing collected packages: globus-sdk, intake_esgf\n",
      "Successfully installed globus-sdk-3.55.0 intake_esgf-2024.12.7\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# on starting the server, we need to run the line below once, as intake_esgf is not yet in our standard pangeo environment\n",
    "%pip install intake_esgf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake_esgf\n",
    "cat = ESGFCatalog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cf_standard_name</th>\n",
       "      <th>variable_units</th>\n",
       "      <th>variable_long_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hfls</th>\n",
       "      <td>surface_upward_latent_heat_flux</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>Surface Upward Latent Heat Flux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hfss</th>\n",
       "      <td>surface_upward_sensible_heat_flux</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>Surface Upward Sensible Heat Flux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rlds</th>\n",
       "      <td>surface_downwelling_longwave_flux_in_air</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>Surface Downwelling Longwave Radiation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsds</th>\n",
       "      <td>surface_downwelling_shortwave_flux_in_air</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>Surface Downwelling Shortwave Radiation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfcWind</th>\n",
       "      <td>wind_speed</td>\n",
       "      <td>m s-1</td>\n",
       "      <td>Near-Surface Wind Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ta</th>\n",
       "      <td>air_temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>Air Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tas</th>\n",
       "      <td>air_temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>Near-Surface Air Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tasmax</th>\n",
       "      <td>air_temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>Daily Maximum Near-Surface Air Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tasmin</th>\n",
       "      <td>air_temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>Daily Minimum Near-Surface Air Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vas</th>\n",
       "      <td>northward_wind</td>\n",
       "      <td>m s-1</td>\n",
       "      <td>Northward Near-Surface Wind</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cf_standard_name variable_units  \\\n",
       "variable_id                                                             \n",
       "hfls                   surface_upward_latent_heat_flux          W m-2   \n",
       "hfss                 surface_upward_sensible_heat_flux          W m-2   \n",
       "rlds          surface_downwelling_longwave_flux_in_air          W m-2   \n",
       "rsds         surface_downwelling_shortwave_flux_in_air          W m-2   \n",
       "sfcWind                                     wind_speed          m s-1   \n",
       "ta                                     air_temperature              K   \n",
       "tas                                    air_temperature              K   \n",
       "tasmax                                 air_temperature              K   \n",
       "tasmin                                 air_temperature              K   \n",
       "vas                                     northward_wind          m s-1   \n",
       "\n",
       "                                     variable_long_name  \n",
       "variable_id                                              \n",
       "hfls                    Surface Upward Latent Heat Flux  \n",
       "hfss                  Surface Upward Sensible Heat Flux  \n",
       "rlds             Surface Downwelling Longwave Radiation  \n",
       "rsds            Surface Downwelling Shortwave Radiation  \n",
       "sfcWind                         Near-Surface Wind Speed  \n",
       "ta                                      Air Temperature  \n",
       "tas                        Near-Surface Air Temperature  \n",
       "tasmax       Daily Maximum Near-Surface Air Temperature  \n",
       "tasmin       Daily Minimum Near-Surface Air Temperature  \n",
       "vas                         Northward Near-Surface Wind  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.variable_info(\"temperature air surface\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23cfbf13424c48f3830dd7fdbe76b2ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "   Searching indices:   0%|          |0/2 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Summary information for 1687 results:\n",
       "mip_era                                                     [CMIP6]\n",
       "activity_drs                                                 [CMIP]\n",
       "institution_id    [IPSL, NASA-GISS, NCAR, MPI-M, MRI, CNRM-CERFA...\n",
       "source_id         [IPSL-CM6A-LR, GISS-E2-1-H, CESM2, GISS-E2-1-G...\n",
       "experiment_id                                          [historical]\n",
       "member_id         [r9i1p1f1, r7i1p1f1, r4i1p1f1, r27i1p1f1, r5i1...\n",
       "table_id          [ImonGre, ImonAnt, Amon, day, 3hr, 6hrPlevPt, ...\n",
       "variable_id                                                   [tas]\n",
       "grid_label                             [grg, gra, gr, gn, gr1, gr2]\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.search(variable_id=\"tas\", experiment_id=\"historical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fbf3920984a4bc0a68dc479e90124f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "   Searching indices:   0%|          |0/2 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Summary information for 1 results:\n",
       "mip_era                [CMIP6]\n",
       "activity_drs            [CMIP]\n",
       "institution_id          [NCAR]\n",
       "source_id              [CESM2]\n",
       "experiment_id     [historical]\n",
       "member_id           [r1i1p1f1]\n",
       "table_id                [Amon]\n",
       "variable_id              [tas]\n",
       "grid_label                [gn]\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.search(\n",
    "    project='CMIP6',\n",
    "    experiment_id='historical',\n",
    "    source_id='CESM2',\n",
    "    variable_id='tas',  # surface air temperature\n",
    "    table_id='Amon',    # monthly atmospheric data\n",
    "    variant_label='r1i1p1f1'  # ensemble member\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e27d0aea7234e77ad702d14ffdb088e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Get file information:   0%|          |0/2 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading 243.0 [Mb]...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77444c7f27d94e55920c52d599c41527",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tas_Amon_CESM2_historical_r1i1p1f1_gn...:   0%|          |0.00/243M [?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "775afe7fa6a34f86bb9aa232ecca697b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Adding cell measures:   0%|          |0/1 [     ?dataset/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e890cc127394daf96830424687978d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "areacella_fx_CESM2_historical_r1i1p1f...:   0%|          |0.00/43.8k [?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsd = cat.to_dataset_dict() # dsd is a dictionary of xarray datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSet dictionary:  {'tas': <xarray.Dataset> Size: 438MB\n",
      "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
      "  * time       (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
      "Dimensions without coordinates: nbnd\n",
      "Data variables:\n",
      "    tas        (time, lat, lon) float32 438MB 245.3 245.3 245.3 ... 245.0 245.0\n",
      "    time_bnds  (time, nbnd) object 32kB ...\n",
      "    lat_bnds   (lat, nbnd) float32 2kB ...\n",
      "    lon_bnds   (lon, nbnd) float32 2kB ...\n",
      "    areacella  (lat, lon) float32 221kB ...\n",
      "Attributes: (12/47)\n",
      "    Conventions:            CF-1.7 CMIP-6.2\n",
      "    activity_id:            CMIP\n",
      "    case_id:                15\n",
      "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
      "    contact:                cesm_cmip6@ucar.edu\n",
      "    creation_date:          2019-01-16T23:34:05Z\n",
      "    ...                     ...\n",
      "    branch_time_in_parent:  219000.0\n",
      "    branch_time_in_child:   674885.0\n",
      "    branch_method:          standard\n",
      "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
      "    activity_drs:           CMIP\n",
      "    member_id:              r1i1p1f1}\n",
      "DataSet:  <xarray.Dataset> Size: 438MB\n",
      "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
      "  * time       (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
      "Dimensions without coordinates: nbnd\n",
      "Data variables:\n",
      "    tas        (time, lat, lon) float32 438MB 245.3 245.3 245.3 ... 245.0 245.0\n",
      "    time_bnds  (time, nbnd) object 32kB ...\n",
      "    lat_bnds   (lat, nbnd) float32 2kB ...\n",
      "    lon_bnds   (lon, nbnd) float32 2kB ...\n",
      "    areacella  (lat, lon) float32 221kB ...\n",
      "Attributes: (12/47)\n",
      "    Conventions:            CF-1.7 CMIP-6.2\n",
      "    activity_id:            CMIP\n",
      "    case_id:                15\n",
      "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
      "    contact:                cesm_cmip6@ucar.edu\n",
      "    creation_date:          2019-01-16T23:34:05Z\n",
      "    ...                     ...\n",
      "    branch_time_in_parent:  219000.0\n",
      "    branch_time_in_child:   674885.0\n",
      "    branch_method:          standard\n",
      "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
      "    activity_drs:           CMIP\n",
      "    member_id:              r1i1p1f1\n",
      "DataArray:  <xarray.DataArray 'tas' (time: 1980, lat: 192, lon: 288)> Size: 438MB\n",
      "array([[[245.32208, 245.32208, ..., 245.32208, 245.32208],\n",
      "        [246.10596, 246.06238, ..., 246.15019, 246.12573],\n",
      "        ...,\n",
      "        [245.02821, 245.0406 , ..., 244.99951, 245.01454],\n",
      "        [244.50035, 244.50319, ..., 244.49379, 244.49722]],\n",
      "\n",
      "       [[232.51073, 232.51073, ..., 232.51073, 232.51073],\n",
      "        [233.30011, 233.26118, ..., 233.32066, 233.31026],\n",
      "        ...,\n",
      "        [244.68976, 244.70775, ..., 244.64677, 244.6693 ],\n",
      "        [243.6899 , 243.6928 , ..., 243.68317, 243.68669]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[234.63194, 234.63194, ..., 234.63194, 234.63194],\n",
      "        [235.37543, 235.35039, ..., 235.38136, 235.37898],\n",
      "        ...,\n",
      "        [256.5771 , 256.58975, ..., 256.5506 , 256.56418],\n",
      "        [256.69495, 256.69467, ..., 256.69556, 256.69522]],\n",
      "\n",
      "       [[246.79817, 246.79817, ..., 246.79817, 246.79817],\n",
      "        [247.46426, 247.42882, ..., 247.48152, 247.47386],\n",
      "        ...,\n",
      "        [244.81926, 244.83385, ..., 244.78955, 244.80447],\n",
      "        [245.01997, 245.01904, ..., 245.02213, 245.021  ]]], dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * lon      (lon) float64 2kB 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
      "  * time     (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
      "Attributes: (12/19)\n",
      "    cell_measures:  area: areacella\n",
      "    cell_methods:   area: time: mean\n",
      "    comment:        near-surface (usually, 2 meter) air temperature\n",
      "    description:    near-surface (usually, 2 meter) air temperature\n",
      "    frequency:      mon\n",
      "    id:             tas\n",
      "    ...             ...\n",
      "    time_label:     time-mean\n",
      "    time_title:     Temporal mean\n",
      "    title:          Near-Surface Air Temperature\n",
      "    type:           real\n",
      "    units:          K\n",
      "    variable_id:    tas\n"
     ]
    }
   ],
   "source": [
    "ds = dsd['tas'] # DataSet: subsetting the dictionary on the variable name gives the xarray DataSet containing the tas data\n",
    "da = dsd['tas']['tas'] # DataArray: selecting the variable tas on the DataSet gives the xarray DataArray of tas data\n",
    "print('DataSet dictionary: ', dsd) \n",
    "print('DataSet: ', ds) # \n",
    "print('DataArray: ', da) # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc8d2b19a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)\n",
    "ds = dsd[\"tas\"][\"tas\"].mean(dim=\"time\") - 273.15  # to [C]\n",
    "ds.plot(ax=ax, cmap=\"bwr\", vmin=-40, vmax=40, \n",
    "        extend='both',cbar_kwargs={\"label\": \"tas [C]\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Widening search criteria if data aren't found\n",
    "\n",
    "The default is for intake-esgf NOT to search over all possible sources of data (for performance reasons). But this means we may come back empty handed on a search when in fact the simulation data is available somewhere. \n",
    "\n",
    "for example, the code below will throw a \"NoSearchResults\" errror even though this simulation is available on the CEDA ESGF node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ba5c099e8d940f0ada7e1c179ec0dfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "   Searching indices:   0%|          |0/2 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NoSearchResults",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNoSearchResults\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCMIP6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexperiment_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mG6sulfur\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43msource_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mUKESM1-0-LL\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvariable_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtas\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# surface air temperature\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtable_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAmon\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# monthly atmospheric data\u001b[39;49;00m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvariant_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr1i1p1f2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# ensemble member\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.12/site-packages/intake_esgf/catalog.py:336\u001b[0m, in \u001b[0;36mESGFCatalog.search\u001b[0;34m(self, quiet, **search)\u001b[0m\n\u001b[1;32m    334\u001b[0m search_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m    335\u001b[0m dfs \u001b[38;5;241m=\u001b[39m ThreadPool(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices))\u001b[38;5;241m.\u001b[39mimap_unordered(_search, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices)\n\u001b[0;32m--> 336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf \u001b[38;5;241m=\u001b[39m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombine_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    337\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    338\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdfs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdisable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbar_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbar_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[43m        \u001b[49m\u001b[43munit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mindex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    342\u001b[0m \u001b[43m        \u001b[49m\u001b[43munit_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSearching indices\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[43m        \u001b[49m\u001b[43mascii\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtotal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_project()\n\u001b[1;32m    350\u001b[0m \u001b[38;5;66;03m# even though we are using latest=True, because the search is distributed, we\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \u001b[38;5;66;03m# may have different versions from different indices.\u001b[39;00m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.12/site-packages/intake_esgf/base.py:213\u001b[0m, in \u001b[0;36mcombine_results\u001b[0;34m(dfs)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(df) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    212\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\x1b\u001b[39;00m\u001b[38;5;124m[36;32msearch end \u001b[39m\u001b[38;5;130;01m\\x1b\u001b[39;00m\u001b[38;5;124m[91;20mno results\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoSearchResults()\n\u001b[1;32m    214\u001b[0m \u001b[38;5;66;03m# retrieve project information about how to combine results\u001b[39;00m\n\u001b[1;32m    215\u001b[0m project_id \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39munique()\n",
      "\u001b[0;31mNoSearchResults\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cat.search(\n",
    "    project='CMIP6',\n",
    "    experiment_id='G6sulfur',\n",
    "    source_id='UKESM1-0-LL',\n",
    "    variable_id='tas',  # surface air temperature\n",
    "    table_id='Amon',    # monthly atmospheric data\n",
    "    variant_label=['r1i1p1f2']  # ensemble member\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution is to run the following two lines, which turn on all ESGF nodes for the search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f12c0e9774104e7c83867a1bd0041b39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "   Searching indices:   0%|          |0/9 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Summary information for 1 results:\n",
       "mip_era                 [CMIP6]\n",
       "activity_drs           [GeoMIP]\n",
       "institution_id           [MOHC]\n",
       "source_id         [UKESM1-0-LL]\n",
       "experiment_id        [G6sulfur]\n",
       "member_id            [r1i1p1f2]\n",
       "table_id                 [Amon]\n",
       "variable_id               [tas]\n",
       "grid_label                 [gn]\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## run the following line to widen search criteria to include all ESGF nodes\n",
    "intake_esgf.conf.set(all_indices=True)\n",
    "cat = ESGFCatalog()\n",
    "\n",
    "\n",
    "cat.search(\n",
    "    project='CMIP6',\n",
    "    experiment_id='G6sulfur',\n",
    "    source_id='UKESM1-0-LL',\n",
    "    variable_id='tas',  # surface air temperature\n",
    "    table_id='Amon',    # monthly atmospheric data\n",
    "    variant_label=['r1i1p1f2']  # ensemble member\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}